Process Mining with Python: Improving processes using Python

Python is a versatile language that you can use for almost anything.

In this post, we’ll explore how to use Python for process mining. Process mining is a technique that allows you to discover, monitor, and improve business processes.

How is Python used in Process Mining?

Python is a multipurpose language that can be used for many different objectives. In data mining, python has gained a huge popularity due to its ability to handle large amounts of data and its flexibility.

In the field of process mining, python can be used to create algorithms that help to discover hidden patterns and relationships in data. This can be useful in a variety of different settings, such as improving business processes or understanding complex systems.

Why use Python for Process Mining?

Python is used in process mining in a number of ways.

Extract data

Python can be used to extract data from process logs. This data can then be used to create process models that can be analyzed to find bottlenecks and inefficiencies. 

Build models

Python can be used to build data-driven process models. These models are based on data collected from processes, and they can be used to improve the efficiency of those processes. 

Python can also be used to build simulation-based process models. These models help businesses simulate and test process changes before they’re implemented. There are many types of process models that can be built with Python.

Optimizations

There are many different ways to optimize a process, and Python can be used to find and create custom solutions that are tailored to the specific needs of the process.

Visualizations

Python can be used to create process visualizations that can be used to communicate process information to stakeholders. In addition, python can also be used to create visualizations of data, which can help to identify inefficiencies and potential improvements.

Below is an example of process visualized with python library Pm4Py (which we will look closer at further down)

Libraries and Tools 

Python has a wide range of libraries that can be used for data mining and process modeling. We will have a closer look at the most popular ones further down in this post. 

Free and open-source

Last but not least, python is a free and open-source programming language with a wide range of applications. Python is easy to learn and has a wide range of libraries that allow you to do everything from data analysis, machine learning and process mining. 

Best Practices for Process Mining with Python

There are a few best practices you should follow to get the most out of your process mining project. Some of the best practices for process mining include:

• Scope: Defining the goal of your process mining project and be clear about the ambitions
• Data: You need to have accurate and up-to-date data. The event logs that you use for process mining should be complete and free of any errors. It’s important to collect and use high-quality data
• Know the process: You need to understand the process that you’re trying to mine. This means having a good grasp of the business process itself, as well as the data that you’re working with. You may need to conduct extensive exploratory analysis
• Actionable insights: Remember to communicate your findings and visualize your results to generate actionable insights. Turn interesting outcomes to results that can be used to improve your process. 

Notice that these best practices apply not only to process mining with python but are general recommendations for all process mining projects. 

Learn Python for Process Mining

If you’re new to python programming and process mining, I recommend starting with python. It might seem obvious, but I like to think of python (or any programming language) as the tool I am using to solve a problem, in this case, a process mining application. So if I want to use the tool on my problem, I must first learn how to handle that tool. 

However, if you already know python and want to see how it can be applied in process mining. I recommend these two tutorials that are great examples 

• Introduction to Process Mining – Towardsdatascience
• Introduction to Process Mining: Transform Transactional Data into Models Using Python – Medium

If you are curios to learn more about process mining, we recommend our Introduction to process mining, or check out all of our posts related to process mining

Python libraries and tools for Process Mining

There are many different libraries and tools that can be used for process mining in Python. 

Some of the most popular ones include 

Pm4py

pm4py is a python library that supports process mining algorithms in python. It is completely open source and intended to be used in both academia and industry projects. pm4py is a product of the Fraunhofer Institute for Applied Information Technology

Example of process discovery using Pm4Py

• PM4Py website
• PM4Py documentation

Disco

Disco is a powerful Python library that makes it easy to work with data. Disco can help you clean, process, and analyze data quickly and easily. It also provides a variety of tools for visualizing data, so you can explore and understand your data in new ways.

• Disco introduction and documentation

PyAlpha

Python tool that generates a Petri net using the Alpha Algorithm from event logs. A Petri net is a graphical tool used to model various systems and is often used to model concurrent and distributed systems. Pyalpha is a powerful tool for process mining with Python. 

• Pyalpha on Github

FAQ: Process Mining Using Python